Questions tagged [advantage-actor-critic]

For questions related to the advantage actor-critic algorithms (that is, actor-critic algorithms that use the "advantage" function).

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How to tune hypeparametes in A2C-ppo?

Im currently working with A2C. The model was able to learn open ai pong, i ran this as a sanity check that i havent made any bugs. Now im trying to make the model play breakout, but still after 10m ...
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1 vote
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Can vanilla multi armed bandit problems be solved by RL algorithms like A2C and PPO?

Let's say we have N bandit machines with some distributions (assume some are gaussian, some are uniform, some are chi squared). We want to maximize rewards in X amount of time. I am aware that ...
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Why is my Advantage Actor Critic agent learning and then forgetting (i.e. the loss drops to zero)?

I am writing my own version of the A2C algorithm in PyTorch, and for the most part my algorithm is learning the simple environments (like CartPole-v1). Unfortunately during the training process, the ...
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A2C: Why do episode rewards reset?

I am training a model using A2C with stable baselines 2. When I increased the timesteps I noticed that episode rewards seem to reset (see attached plot). I don´t understand where these sudden decays ...
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How can I compare the results of AC1 with the results of A3C (on the CartPole environment)?

I am implementing A3C for the CartPole environment. I want to compare the results I got from A3C with the ones I got from AC1. The problem is I don't know which process to look at. If I use, let's say,...
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-2 votes
1 answer
192 views

Policy Gradient ( Advantage actor-critic) for multiple simultaneous continuous actions

i'm trying to solve a problem in which i need to carry out reinforcement learning with multiple simultaneous actions in continuous action space . i checked the multiagent structure; however, im trying ...
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1 vote
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What is the difference between step_model and train_model in the OpenAI implementation of the A2C algorithm?

I'm struggling a little with understanding the OpenAI implementation of A2C in the baselines (version 2.9.0) package. From my understanding, one ...
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2 votes
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59 views

Problems with gradient-biased actor critic methods

To my knowledge, there are at least 6 different variants of Actor Critic: \begin{array}{l l l l} \text{actor gradient} & \text{critic gradient} & \text{actor gradient biased} & \text{name} ...
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4 votes
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What is the difference between vanilla policy gradient with a baseline as value function and advantage actor-critic?

What is the difference between vanilla policy gradient (VPG) with a baseline as value function and advantage actor-critic (A2C)? By vanilla policy gradient I am specifically referring to spinning up's ...
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1 vote
1 answer
102 views

Why is the "reward to go" replaced by Q instead of V, when transitioning from PG to actor critic methods?

While transitioning from simple policy gradient to the actor-critic algorithm, most sources begin by replacing the "reward to go" with the state-action value function (see this slide 5). I am not ...
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4 votes
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What is the advantage of using more than one environment with the advantage actor-critic?

make_env = lambda: ptan.common.wrappers.wrap_dqn(gym.make("PongNoFrameskip-v4")) envs = [make_env() for _ in range(NUM_ENVS)] Here is a code you can look at. ...
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2 votes
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What is the original source of the TD Advantage Actor-Critic algorithm?

What is the original source of the TD Advantage Actor-Critic algorithm? I found this tutorial really helpful for learning the algorithm. However, what is the original source of this algorithm?
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Is A2C loss function taking smaller steps for larger mistakes?

A2C loss is usually defined as advantage * (-log(actor_predictions)) * target where target is a one-hot vector (with some ...
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  • 141
3 votes
1 answer
574 views

How to set the target for the actor in A2C?

I did a simple Actor-Critic implementation in Keras using 2 networks where the critic learns the Q-Values of every action, and the actor predicts probabilities for choosing each action. In training, ...
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  • 141
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2 answers
417 views

Why I got the same action when testing the A2C?

I'm working on an advantage actor-critic (A2C) reinforcement learning model, but when I test the model after I trained for 3500 episodes, I start to get almost the same action for all testing episodes....
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3 votes
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576 views

Implementation of PPO - Value Loss not converging, return plateauing

Copy from my reddit post: (Sorry if this does not fit here, please tell me and i delete it) Help regarding I'm working on an implementation of PPO, which i plan to use in my (Bachelors) Thesis. To ...
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3 votes
1 answer
414 views

What is the difference between A2C and running an agent in an environment vector?

I've implemented A2C. I'm now wondering why would we have multiple actors walk around the environment and gather rewards, why not just have a single agent run in an environment vector? I personally ...
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16 votes
3 answers
8k views

What is the difference between actor-critic and advantage actor-critic?

I'm struggling to understand the difference between actor-critic and advantage actor-critic. At least, I know they are different from asynchronous advantage actor-critic (A3C), as A3C adds an ...
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